Modeling, Sensing, Estimation, and Control of Soft and Continuum Robots

Recent progress in materials, flexible sensors, and embedded intelligence has significantly accelerated the development of soft and continuum robots. Compared with conventional rigid robots, these systems offer superior compliance, dexterity, and adaptability but introduce unique challenges in kinematic and dynamic modeling, state estimation, sensing, and control due to their continuously deformable structures and strong nonlinearities. At the same time, emerging technologies such as machine learning, foundation models, digital twins, physics-informed learning, and multimodal perception are creating new opportunities to improve the performance, robustness, and autonomy of these robotic systems. As the field continues to evolve rapidly, there is an increasing need to integrate advances in modeling, estimation, sensing, and control into unified frameworks that enable reliable deployment in real-world applications.Soft and continuum robots have attracted increasing attention due to their intrinsic compliance, adaptability, and safety in interacting with humans and unstructured environments. These characteristics make them promising candidates for applications including medical intervention, industrial inspection, wearable robotics, precision manipulation, and agricultural automation. However, their highly deformable structures, nonlinear dynamics, infinite-dimensional behavior, and complex environmental interactions present significant challenges for accurate modeling, reliable state estimation, and robust control. The goal of this Research Topic is to bring together recent advances that address these challenges through innovative modeling, estimation, and control methodologies. We aim to provide a multidisciplinary platform for researchers from robotics, control engineering, mechanical engineering, artificial intelligence, and related fields to present theoretical developments, computational methods, experimental validation, and real-world applications. By promoting the integration of physics-based models, data-driven approaches, intelligent perception, and advanced control strategies, this collection seeks to advance the next generation of intelligent soft and continuum robotic systems capable of operating safely, autonomously, and efficiently in complex environments.This Research Topic welcomes original research articles and review articles covering theoretical, computational, and experimental advances in soft and continuum robotics. Topics of interest include, but are not limited to:- Kinematic and dynamic modeling of soft and continuum robots- State, shape, force, and contact estimation- Physics-based, data-driven, and hybrid modeling approaches- Learning-based, adaptive, robust, and model predictive control- Reinforcement learning and intelligent control strategies- Sensor fusion, multimodal perception, and tactile sensing- System identification and digital twin technologies- Variable stiffness and compliant robotic systems- Motion planning and autonomous manipulation- Human–robot interaction and shared control- Medical, industrial, agricultural, and service robotic applications- Experimental validation, benchmarking, and open-source platformsContributions presenting novel algorithms, theoretical analyses, hardware innovations, or integrated robotic systems are all encouraged. Interdisciplinary studies that bridge control engineering with artificial intelligence, sensing technologies, biomechanics, or materials science are particularly welcome. Topic editors: Yeman Fan, Bowen Yi, Xing Wang Source: https://www.frontiersin.org/research-topics/83906/modeling-sensing-estimation-and-control-of-soft-and-continuum-robots